Bayesian joint models for longitudinal and survival data

TL;DR

This paper reviews Bayesian joint models that integrate longitudinal and survival data, discussing their formulation, basic models, extensions, and Bayesian topics to enhance understanding and application.

Contribution

It provides a comprehensive overview of Bayesian joint models, including their formulation, basic structure, and potential extensions, highlighting recent developments in the field.

Findings

Formulation of Bayesian joint models for longitudinal and survival data

Discussion of a basic mixed linear and Cox model framework

Outline of extensions and Bayesian topics in joint modeling

Abstract

This paper takes a quick look at Bayesian joint models (BJM) for longitudinal and survival data. A general formulation for BJM is examined in terms of the sampling distribution of the longitudinal and survival processes, the conditional distribution of the random effects and the prior distribution. Next a basic BJM defined in terms of a mixed linear model and a Cox survival regression models is discussed and some extensions and other Bayesian topics are briefly outlined.